Recently, there has been increased interest in real-time forecasts of the real price of crude oil. Standard oil price forecasts based on reduced-form regressions or based on oil futures prices do not allow consumers of forecasts to explore how much the forecast would change relative to the baseline forecast under alternative scenarios about future oil demand and oil supply conditions. Such scenario analysis is of central importance for end-users of oil price forecasts interested in evaluating the risks underlying these forecasts. We show how policy-relevant forecast scenarios can be constructed from recently proposed structural vector autoregressive models of the global oil market and how changes in the probability weights attached to these scenarios affect the upside and downside risks embodied in the baseline real-time oil price forecast. Such risk analysis helps forecast users understand what assumptions are driving the forecast. An application to real-time data for December 2010 illustrates the use of these tools in conjunction with reduced-form vector autoregressive forecasts of the real price of oil, the superior realtime forecast accuracy of which has recently been established.